Full Stack Data Science Course

• Python Basic Building
• Python Keywords and identifiers
• Comments, indentation, statements
• Variables and data types in Python
• Standard Input and Output

• Operators
• Control flow: if else elif
• Control flow: while loop
• Control flow: for loop
• Control flow: break & continue
• Python Data Structures

• Strings
• Lists, Lists comprehension
• Tuples, Sets
• Dictionary, Dictionary Comprehension

• Python Functions
• Python Builtin Functions.
• Python Userdefined Functions.
• Python Recursion Functions.

• Python Lambda Functions.
• Python Exception Handling, 
• Logging And Debugging

• Exception Handling 
• Custom Exception Handling
• Logging With Python
• Debugging With Python
• Python OOPS
• Python Objects And Classes
• Python Constructors
• Python Inheritance
• Abstraction In Python
• Polymorphism in Python
• Encapsulation in Python

• File Handling
• Create 
• Read
• Write
• Append

• Introduction to NumPy
• NumPy Array
• Creating NumPy Array
• Array Attributes, 
• Array Methods
• Array Indexing, 
• Slicing Arrays
• Array Operation
• Iteration through Arrays

• Introduction to Pandas
• Pandas Series
• Creating Pandas Series
• Accessing Series Elements
• Filtering a Series
• Arithmetic Operations
• Series Ranking and Sorting
• Checking Null Values
• Concatenate a Series

• Data Frame Manipulation
• Pandas Dataframe 
• Introduction Dataframe Creation
• Reading Data from Various Files
• Understanding Data
• Accessing Data Frame Elements using Indexing
• Dataframe Sorting
• Ranking in Dataframe
• Dataframe Concatenation
• Dataframe Joins
• Dataframe Merge
• Reshaping Dataframe
• Pivot Tables, 
• Cross Tables
• Dataframe Operations

• Checking Duplicates
• Dropping Rows and Columns
• Replacing Values
• Grouping Dataframe
• Missing Value Analysis & Treatment
• Visualization using Matplotlib
• Plot Styles & Settings
• Line Plot, 
• Multiline Plot
• Matplotlib Subplots
• Histogram, Boxplot
• Pie Chart ,Scatter Plot
• Visualization using Seaborn
• Strip Plot ,Distribution Plot
• Joint Plot, 
• Violin Plot, 
• Swarm Plot
• Pair Plot,
• Count Plot
• Heatmap
• Visualization using Plotly
• Boxplot
• Bubble Chart
• Violin Plot
• 3D Visualization

• EDA and Feature Engineering
• Introduction of EDA
• Dataframe Analysis using Groupby
• Advanced Data Explorations

• Working with SQL Using MySQL 
• Work Bench / SQL Server"
• USE, DESCRIBE, 
• SHOW TABLES
• SELECT, INSERT
• UPDATE & DELETE
• CREATE TABLE
• ALTER: ADD, MODIFY, DROP
• DROP TABLE, TRUNCATE, DELETE
• LIMIT, OFFSET
• ORDER BY
• DISTINCT
• WHERE Clause
• HAVING Clause
• Logical Operators
• Aggregate Functions: COUNT, MIN, MAX, AVG, SUM
• GROUP BY
• SQL Primary And Foreign Key
• Join and Natural Join
• Inner, Left, Right and Outer joins

• Advance SQL
• Subqueries/Nested Queries/Inner Queries
• SQL Function And Stored Procedures
• SQL Window Function
• CTE In SQL
• Normalization In SQL

• Basic Math
• Linear Algebra
• Probability
• Calculus
• Develop a comprehensive understanding of coordinate geometry and linear algebra.
• Build a strong foundation in calculus, including limits, derivatives, and integrals.

• Descriptive Statistics
• Sampling Techniques
• Measure of Central Tendency
• Measure of Dispersion
• Skewness and Kurtosis
• Random Variables
• Bassells Correction Method
• Percentiles and Quartiles
• Five Number Summary
• Gaussian Distribution
• Lognormal Distribution
• Binomial Distribution
• Bernoulli Distribution

• Inferential Statistics
• Standard Normal Distribution 
• ZTest
• TTest
• ChiSquare Test
• ANOVA / FTest
• Introduction to Hypothesis Testing
• Null Hypothesis
• Alternet Hypothesis

• Probability Theory
• What is Probability?
• Events and Types of Events
• Sets in Probability
• Probability Basics using Python
• Conditional Probability
• Expectation and Variance

• Introduction to Machine Learning
• Machine Learning Modelling Flow
• "Supervised and Unsupervised 
• Types of Machine Learning Algorithms

• Linear Regression using OLS
• Introduction of Linear Regression
• Types of Linear Regression
• OLS Model
• Math behind Linear Regression
• Decomposition Variability
• Metrics to Evaluate Model
• Feature Scaling
• Feature Selection
• Regularisation Techniques
• Ridge Regression 
• Lasso Regression
• ElastivNet Regression

• Optimisation Techniques
• What is Optimisation?
• Gradient Descent
• Adagrad Algorithm
• Adam Algorithm
• Linear Regression with SGD
• Prerequisites

• Introduction to Stochastic Gradient Descent (SGD)
• Preparation for SGD
• Workflow of SGD
• Implementation of SGD on Linear Regression

• Logistic Regression
• Maximum Likelihood Estimation
• "Logistic Regression Using Sigmoid 
• Activation Function"
• Performance Metrics 
• Confusion Matrix
• Precision, Recall, F1Score
• Receiver Operating Characteristic Curve

• KNN
• Euclidean Distance
• Manhattan Distance
• Implementation for KNN

• SVM
• Support Vector Regression
• Support Vector Classification
• Polynomial Kernel
• Cost Function
• GridSerchCV

• Decision Trees
• Decision Tree for Classification
• Decision Tree for Regression
• ID3 Algorithm
• CART Algorithm
• Entropy
• Gini Index
• Information Gain
• Decision Tree: Regression
• Mean Square Error
• PrePruning and PostPruning

• Naive Bayes
• Introduction to Bayes Theorem
• Explanation for naive bayes

• Ensemble Technique
• Bagging
• Random Forest Classifier
• Random Forest Regression
• Random Forest – Why & How?
• Feature Importance
• Advantages & Disadvantages

• Boosting
• Bootstrap Aggregating
• AdaBoost
• XgBoost
• Project For Random Forest
• Project Penguin Classification
• Project Texi Prediction

• Kmeans Clustering
• Prerequisites
• Cluster Analysis
• Kmeans
• Implementation of Kmeans
• Pros and Cons of Kmeans
• Application of Kmeans
• Elbow Method
• Model building for Kmeans Clustering

• Hierarchical Clustering
• Types of Hierarchical Clustering
• Dendrogram
• Pros and Cons of Hierarchical Clustering
• Model building for Hierarchical Clustering

• DBSCAN Clustering
• Introduction for DBSCAN Clustering
• implementation of DBSCAN

• Principal Components Analysis
• Prerequisites
• Introduction to PCA
• Principal Component
• Implementation of PCA
• Case study
• Applications of PCA
• Project on PCA

• Time Series Modelling
• Understand Time Series Data
• Visualising Time Series Components
• Exponential Smoothing
• ARIMA
• SARIMA
• SARIMAX
• Project on Forecasting
• Cloud Basics
• ML on Cloud

Artificial Neural Network (ANN)

• Biological and Artificial Neurons
• Activation Functions
• Perceptron
• Feed Forward Network
• Multilayer Perceptron (MLP)
• Back Propagation, Deep ANN
• Optimisation Algorithms
• Gradient Descent
• Stochastic Gradient Descent (SGD)
• MiniBatch Stochastic Gradient Descent
• Stochastic Gradient Descent with Momentum
• AdaGrad, RMSProp , Adam
• Batch Normalisation

• KERAS
• What is Keras?
• How to Install Keras?
• Why to Use Keras?
• Different Models of Keras
• Preprocessing Methods
• What are the Layers in Keras?

• Tensorflow 2.0
• TensorFlow in Realtime Applications
• Advantages of TensorFlow
• How to Install TensorFlow
• TensorFlow 1x vs TensorFlow 2.0
• Eager Execution in TensorFlow 2.0

• Convolutional Neural Network(CNN)
• Introduction to Computer Vision
• Convolutional Neural Network
• Architecture of Convolutional network
• Image as a Matrix, Convolutional Layer
• Feature Detector & Feature Maps
• Pooling Layer, Max Pooling
• Min Pooling, 
• Avg Pooling
• Flattening Layer, Padding, Striding
• Image Augmentation
• Basics of Digital Images

• Recurrent Neural Network (RNN)
• RNN Network Structure
• Different Types of RNNs
• Bidirectional RNN
• Limitations of RNN

• Natural Language Processing 
• NLTK
• What is NLP?
• Typical NLP Tasks
• Morphology
• Sentence Segmentation & Tokenization
• Pattern Matching with Regular Expression
• Stemming, Lemmatization
• Stop Words Removal (English)
• Corpora/Corpus
• Context Window – Bigram, Ngram
• Applications of NLP
• Introduction to the NLTK Library
• Processing Raw Text
• Regular Expression
• Normalising Text
• Processing Raw Text – Tokenise Sentences
• String Processing with Regular Expression, Normalising Text
• Extracting Features from Text
• Bag-of-Words(BoW), TF-IDF
• Similarity score Cosine similarity

• Computer Vision
• Image Formation
• Sampling and Quantisation
• Image Processing – flipping, cropping, rotating, scaling
• Image statistics & Histogram
• Spatial Resolution
• Gray level/Intensity Resolution
• Spatial Filtering
• Convolution
• Smoothing, Sharpening
• Color Space Conversion & Histogram

• Thresholding for Binarization
• Morphological Operations
• Image Gradient
• Bounding Box
• Sobel’s Edge Detection Operator
• Template Matching
• Image Feature – Keypoint and Descriptor
• Harris Corner Detector
• Object Detection with HoG
• Stream Video Processing with OpenCV

• Advance NLP
• "Use Logistic Regression, 
• Naive Bayes and Word vectors to implement Sentiment Analysis"
• R-CNN
• RNN
• Encoder-Decoder
• Transformer
• Reformer
• Embeddings
• Information Extraction
• LSTM
• Attention
• Named Entity Recognition
• Transformers
• HuggingFace
• BERT
• Text Generation
• Named Entity Recognition
• GRU
• Siamese Network in TensorFlow
• Self Attention Model
• Advanced Machine Translation of Complete Sentences
• Text Summarization

• Prompt Engineering
• Why Prompt Engineering?
• ChatGPT
• Few Standard Definitions:
• Label, Logic
• Model Parameters (LLM Parameters)
• Basic Prompts and Prompt Formatting
• Elements of a Prompt, Context
• Task Specification
• Constraints
• General Tips for Designing Prompts:
• Be Specific ,Keep it Concise
• Be Contextually Aware
• Test and Iterate
• Prompt Engineering Use Cases
• Information Extraction
• Text Summarization
• Question Answering
• Code Generation
• Text Classification
• Prompt Engineering Techniques
• N-shot Prompting
• Zero-shot Prompting
• Chain-of-Thought (CoT) Prompting
• Generated Knowledge Prompting

• Introduction of MLOps
• What and why MLOps
• MLOps fundamentals
• MLOps vs DevOps
• Why DevOps is not sufficient for MLOps
• Challenges in traditional ML Pipeline
• DevOps and MLOps tools and platform
• What is SDLC?
• Types of SDLC
• Waterfall vs AGILE vs DevOps vs MLOps

• MLOps Foundation
• Fundamental of Linux for MLOps and data scientist
• Important Linux Commands
• Source code managements using GIT
• GIT configuration and GIT commands
• YAML for Configuration Writing
• YAML vs JSON Schema
• Docker for Containers
• Docker Basic Command, Dockerhub, Dockerfile
• Cloud Computing and Cloud Infrastructure
• Cloud Service Provider- AWS, GCP, AZURE
• Data Managements and Versioning with DVC
• Monitoring, Alerting, Retraining With Grafana and
• prometheus
• Experiment tracking with MLFLOW
• Model Serving With BENTOML

• End to End project implementation with Deployment implementation with Deployment
• Understanding Machine learning Workflow and Project Setup
• Project Template Setup with GitHub
• Modular workflow Introduction and Implementation
• Understanding the Training Pipeline and Its Components

• Data Ingestion, Data Transformation Model Trainer Model
• Evaluation
• Creating Prediction Pipeline and End Point Creation
• Continues Integration, Continues Delivery and Continues
• Training understanding and Project Deployment

• Prompt Engineering
• Why Prompt Engineering?
• ChatGPT
• Few Standard Definitions:
• Label
• Logic
• Model Parameters (LLM Parameters)
• Basic Prompts and Prompt Formatting
• Elements of a Prompt:
• Context
• Task Specification
• Constraints
• General Tips for Designing Prompts:
• Be Specific
• Keep it Concise
• Be Contextually Aware
• Test and Iterate
• Prompt Engineering Use Cases
• Information Extraction
• Text Summarization
• Question Answering
• Code Generation
• Text Classification
• Prompt Engineering Techniques
• N-shot Prompting
• Zero-shot Prompting
• Chain-of-Thought (CoT) Prompting
• Generated Knowledge Prompting

• Computer Vison
• Convolution Neural Networks (CNN)
• Why CNN? Building an Intuition for CNN
• CNN, Kernels, Channels, Feature Maps, Stride, Padding
• Receptive Fields, Image Output Dimensionality Calculations, MNIST Dataset
• Explorations with CNN
• MNIST CNN Intuition, Tensorspace.js, CNN Explained, CIFAR 10 Dataset Explorations with CNN
• Dropout & Custom Image Classification for Cat and Dog Datasets
• Deployment in Heroku, AWS or Azure

• CNN Architectures
• LeNet-5
• AlexNet, VGGNet
• Inception, ResNet
• Data Augmentation
• Benefits of Data Augmentation
• Exploring Research Papers
• Exploring Augmentor

• Object Detection Basics
• What is Object Detection?
• Competitions for Object Detection
• Bounding Boxes
• Bounding Box Regression
• Intersection over Union (IoU)
• Precision & Recall
• What is Average Precision?
• Practical Training using Tensorflow1.x
• Custom Model Training in TFOD1.x
• Our Custom Dataset
• Doing Annotations or labelling data
• Pretrained Model from Model Zoo
• Files Setup for Training
• Export Frozen Inference Graph
• Inferencing with our trained model in Colab, Training in Local
• Inferencing with our trained model in Local

• Practical Training using Tensorflow2.x
• Introduction to TFOD2.x
• Using the Default Colab Notebook
• Google Colab & Drive Setup

• Visiting TFOD2.x Model Garden
• Inference using Pretrained Model
• Inferencing in Local with a pretrained model

• Practical Object Detection Using YOLO V5
• Introduction for YoloV5
• YoloV5 Google Colab Setup
• Inferencing using Pre-Trained Model

• Prompt Engineering
• Why Prompt Engineering?
• ChatGPT
• Few Standard Definitions:
• Label
• Logic
• Model Parameters (LLM Parameters)
• Basic Prompts and Prompt Formatting
• Elements of a Prompt:
• Context
• Task Specification
• Constraints
• General Tips for Designing Prompts:
• Be Specific
• Keep it Concise
• Be Contextually Aware
• Test and Iterate
• Prompt Engineering Use Cases
• Information Extraction
• Text Summarization
• Question Answering
• Code Generation
• Text Classification
• Prompt Engineering Techniques
• N-shot Prompting
• Zero-shot Prompting
• Chain-of-Thought (CoT) Prompting
• Generated Knowledge Prompting

• Generative AI
• Why are generative models required?
• Understanding generative models and their significance
• Generative AI v/s Discriminative Models
• Recent advancements and research in generative AI
• Gen AI end-to-end project lifecycle
• Key applications of generative models

• Text Preprocessing and Word Embedding
• Segmentation and Tokenization
• Change Case, Spell Correction
• Stop Words Removal, Punctuations Removal, Remove White
• spaces, Stemming and Lemmatization
• Parts of Speech Tagging
• Text Normalization, Rephrase Text
• One hot encoding, 
• Index-based encoding
• Bag of words, 
• TF-IDF
• Word2Vec, 
• FastText
• N-Grams, Elimo
• Bert-based encoding

• Large Language Models(LLM)
• In-depth intuition of Transformer-Attention all your need Paper
• Guide to complete transformer tree
• Transformer Architecture
• Application and use cases of LLMs
• Transfer learning in NLP
• Pre-trained transformer-based models
• How to perform finetuning of pre trained transformer based models
• Mask language modeling

• BERT- Google, GPT- OpenAI
• T5- Google
• Evaluations Matrixs of LLMs models
• GPT-3 and 3.5 Turbo use cases
• Learn how Chatgpt trained
• Introduction to Chatgpt- 4

• Hugging face And its Applications
• Hugging Face Transformers
• Hugging face API key generation
• Hugging Face Transfer learning models based on the state-of-the-art transformer architecture
• Fine-tuning using a pre-train models
• Ready-to-use datasets and evaluation metrics for NLP.
• Data Processing, Tokenizing and Feature Extraction with
• Standardizing the Pipelining
• Training and callbacks
• Language Translation with Hugging Face Transformer

• Generative AI with LLMs and LLM Powered Applications
• Text summarization with hugging face
• Language Translation with Hugging Face Transformer
• Text to Image Generation with LLM with hugging face
• Text to speech generation with LLM with hugging face

• Guide to Open AI and its Ready to Use Models with Application
• What is OpenAI API and how to generate OpenAI API key?
• Installation of OpenAI package
• Experiment in the OpenAI playground
• How to setup your local development environment
• Different templates for prompting
• OpenAI Models GPT-3.5 Turbo DALL-E 2, Whisper, Clip,
• Davinci and GPT-4 with practical implementation
• OpenAI Embeddings and Moderation with Practical
• Implementation of Chat completion API,

• Functional calling and Completion API
• How to manage the Tokens
• Different Tactics for getting an Optimize result
• mage Generation with OpenAI LLM model
• Speech to text with OpenAI
• Use of Moderation for content complies with OpenAI
• Understand rate limits, error codes in OpenAPI
• OpenAI plugins connect ChatGPT to third-party applications.
• How to do fine-tuning with custom data
• Project: Finetuning of GPT-3 model for text classification
• Project: Telegram bot using OpenAI API with GPT-3.5 turbo
• Project: Generating YouTube Transcript with Whisper
• Project: Image generation with DALL-E
• Prompt Engineering Mastering with OpenAI

• Introduction to Prompt Engineering
• Different templates for prompting
• Prompt Engineering: What & Why?
• Prompt Engineering & ChatGPT Custom Instructions
• The Core Elements Of A Good Prompt
• Which Context Should You Add?
• Zero- One- & Few-Shot Prompting
• Using Output Templates
• Providing Cues & Hints To ChatGPT
• Separating Instructions From Content
• Ask-Before-Answer Prompting
• Perspective Prompting
• Contextual Prompting
• Emotional Prompting
• Laddering Prompting
• Using ChatGPT For Prompting
• Find Out Which Information Is Missing
• Self-evaluative Prompting
• ChatGPT-powered Problem Splitting
• Reversing Roles
• More Prompts & Finding Prompt Inspirations
• Super Prompts Like CAN & DAN

• Vector database with Python for LLM Use Cases
• Storing and retrieving vector data in SQLite
• Chromadb local vector database part1 setup and data insertion
• Query vector data
• Fetch data by vector id
• Database operation: create, update, retrieve, deletion, insert and
• update
• Application in semantic search
• Building AI chat agent with langchain and openai
• Weviate Vector Database
• Pinecone Vector Database

• Hands-on with LangChain
• Practical Guide to LlamaIndex with LLMs
• Bonus: Additional Productive Tools to Explore
• Chainlit ( async Python framework)
• LIDA (Automatic Generation of Visualizations and
• Infographics)
• Slidesgo ( AI Presentation Maker )
• Content Creation (Jasper, Copy.ai, Anyword)
• Grammar checkers and rewording tools (Grammarly, Wordtune,
• ProWritingAid)
• Video creation (Descript, Wondershare Filmora, Runway)
• Image generation (DALL·E 2, Midjourney)
• Research (Genei, Aomni)

 Creating a structured framework for data science case studies helps in effectively presenting your work and its impact. Here’s a comprehensive framework you can follow:

• Introduction
• Overview: Provide a brief introduction to the project, including its objectives and significance.
• Problem Statement: Clearly define the problem or opportunity your project aims to address.

• Data Collection and Pre-processing
• Data Sources: Describe where the data came from and its characteristics (structured/unstructured).
• Data Cleaning: Detail the steps taken to clean the data, handle missing values, and address outliers.
• Feature Engineering: Explain how features were selected or engineered to improve model performance.

• Exploratory Data Analysis (EDA)
• Summary Statistics: Present key statistics and distributions of the data.
• Data Visualization: Use charts, graphs, and plots to explore relationships and patterns in the data.
• Insights: Highlight any initial insights gained from the EDA phase.

• Machine Learning or Statistical Models
• Model Selection: Justify the choice of machine learning algorithms or statistical models used.
• Model Training: Explain how models were trained and validated using appropriate techniques (e.g., cross-validation).
• Hyperparameter Tuning: Discuss the process of optimizing model hyperparameters for better performance.

• Evaluation Metrics
• Performance Metrics: Specify the metrics used to evaluate model performance (e.g., accuracy, precision, recall, F1-score).
• Benchmarking: Compare your model’s performance against baseline models or industry standards.

• Results
• Model Performance: Present the results of your models, including metrics and any visualizations that support your findings.
• Key Findings: Summarize the main findings and insights derived from your analysis.

• Deployment and Implementation
• Deployment Strategy: Describe how the model or solution was deployed in a real-world environment.
• Integration: Discuss any challenges faced during deployment and how they were overcome.
• Scalability: Address the scalability of your solution and its potential for future growth.

• Impact and Conclusion
• Business Impact: Quantify the impact of your project in terms of business outcomes or improvements.
• Lessons Learned: Reflect on challenges encountered and lessons learned during the project.
• Future Work: Suggest potential future enhancements or extensions to your work.

• Documentation and Presentation
• Document Structure: Ensure clarity and coherence in presenting each section of the case study.
• Visual Aids: Use visuals (charts, graphs) to enhance understanding and convey key points effectively.
• Narrative Flow: Create a compelling narrative that guides the reader through your data science journey.

• References and Acknowledgments
• Data Sources: Provide citations or acknowledgments for datasets, tools, or libraries used.
• Contributors: Acknowledge team members, advisors, or stakeholders who contributed to the project.